Abstract. With the increasing success and commercial integration of Volunteered Geographic Information (VGI), the focus shifts away from coverage to data quality and homogeneity. Within the last years, several studies have been published analyzing the positional accuracy of features, completeness of specific attributes, or the topological consistency of line and polygon features. However, most of these studies do not take geographic feature types into account. This is for two reasons. First, and in contrast to street networks, choosing a reference set is difficult. Second, we lack the measures to quantify the degree of feature type miscategorization. In this work, we present a methodology to analyze the spatial-semantic interaction of point features in Volunteered Geographic Information. Feature types in VGI can be considered special in both, the way they are formed and the way they are applied. Given that they reflect community agreement more accurately than top-down approaches, we argue that they should be used as the primary basis for assessing spatial-semantic interaction. We present a case study on a spatial and semantic subset of OpenStreetMap, and introduce a novel semantic similarity measure based on the change history of OpenStreetMap elements. Our results set the stage for systems that assist VGI contributors in suggesting the types of new features, cleaning up existing data, and integrating data from different sources.

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Semantic similarity measurement has been an active research area in GIScience and the Semantic Web for many years. However, implementations of these measures were largely missing, not publicly available, or tailored to specific application needs. To foster the application of similarity reasoning in information retrieval, ontology engineering, and spatial decision support, we implemented the SIM-DL semantic similarity server as well as a plug-in for the popular Protégé ontology editor. While SIM-DL has been successfully applied to several application areas, the implemented similarity theory was largely structural, could not handle concept and instance similarity within the same framework, and was based on a Protégé version and DIG interface that have been re-engineered over the last years. This paper introduces a new version, called SIM-DLA, engineered from scratch to addresses these shortcomings. It is based on our new similarity theory, can handle inter-instance and interconcept similarity using the same functions and alignments, and is available for the new Protégé version 4.1.

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Feature types play a crucial role in understanding and analyzing geographic information. Usually, these types are defined, standardized, and controlled by domain experts and cover geographic features on the mesoscale level, e.g., populated places, forests, or lakes. While feature types also underlie most Location-Based Services (LBS), assigning a consistent typing schema for Points Of Interest (POI) across different data sets is challenging. In case of Volunteered Geographic Information (VGI), types are assigned as tags by a heterogeneous community with different backgrounds and applications in mind. Consequently, VGI research is shifting away from data completeness and positional accuracy as quality measures towards attribute accuracy. As tags can be assigned by everybody and have no formal or stable definition, we propose to study category tags via indirect observations. We extract user check-ins from massive real-world data crawled from Location-based Social Networks to understand the temporal dimension of Points Of Interest. While users may assign different category tags to places, we argue that their temporal characteristics, e.g., opening times, will show distinguishable patterns.1 The Infrastructure for Spatial Information in the European Community: http://inspire.jrc.ec.europa.eu/ 2 See OSM wiki at http://wiki.openstreetmap.org/wiki/ Map_Features as an example.